Paper id 36201507

International Journal of Research in Advent Technology, Vol.3, No.6, June 2015
E-ISSN: 2321-9637
32
Minutia Cylindrical Code Based Approach for
Fingerprint Matching
Dilip Tamboli1
, Mr.Sandeep B Patil 2
, Dr.G.R.Sinha 3
1
P.G. Scholar, Department of Electronics & Telecommunication Engg. SSGI Bhilai, C.G.India
2
P.G. Scholar, Department of Electronics & Telecommunication Engg. SSGI Bhilai, C.G.India2
3
Assosiate Director & Prof., Department of Electronics & Telecommunication Engg. SSGI Bhilai, C.G.India3
diliptamboli_123@yahoo.com
1
, Patilsandeepb1212@gmail.com
2
,drgrsinha@ssgi.edu.in3
Abstract- Impression from the fingers and its matching is one of the important task of law enforcing body.
Minutia extraction from the fingerprint image decides the accuracy of the matching. Method presented in this
paper is also very efficient in matching the algorithm. Minutia cylindrical code(MCC) which is local descriptor
of the fingerprint image is used for matching the fingerprint image. MCC, codes the local direction and distance
between the minutia and hence invariants to the scale and rotation. False Acceptance ratio (FAR) and False
Rejection ration(FRR) is also computed to test the accuracy of the proposed method.
Index Terms- Minutia, FFT, MCC, Euclidean distance
1. INTRODUCTION
Recognition of fingerprint is one of the complicated
pattern recognition problems which is being studied
for the last 40 years.
Though various efficient algorithm have been
designed for fingerprint matching, it cannot be
concluded that this problem has solved. Accuracy,
interoperability and computational efficient algorithm
are still an open issue [1] in fingerprinting matching.
Most of the current fingerprint matching algorithms
are based on the minutia. Special ridge pattern are
called minutia. Ridge ending and ridge bifurcation are
some of the minutia.
In the past, minutia matching is considered as the two
dimensional pattern matching problem for aligning the
two minutia pair. This forced the researcher to find all
the possible transformation for two minutia matching.
Hough transform is one of the solution of this
problem[2][3]. High computational cost and lack of
robustness are some the problems of the global
minutia matching algorithm.
In the last few years, these problem is addressed by
introducing the local minutia descriptor and its
matching.
The characteristics of the local minutia descriptor is
such that it is invariants to the global transformation
and therefore appropriate for matching without the
need of global alignment.
Since local minutia descriptor based matching is based
on the arrangement of the local property therefore it
excludes the global feature and give better results.
Global matching algorithms have some of the benefits
which is not possible in case of local descriptor based
matching and hence an hybrid approach can be applied
to get the benefit of both the algorithm.
In this types of approach, first of all the local structure
is used to match the minutia quickly and robustly. In
the next step, matching at global level is performed for
validation purpose.
The evaluation of the minutia matching based on the
local structure passed in three stages. Stage one
correspond to the earlier approach in which local
structure are formed by considering the minutia lies
inside some regions. In this approach no global
validation was performed [4][5]. The approach
adopted by the [6] and [7] comes in the second stage.
[6] and [7] were the first to establishe a relationship
between minutia and its neighbourhood in term of
invariant angle distance and structure. In this stage,
global validation was also performed .
Third stage comprises of the method proposed by
Jiang and Yau[6] and Ratha [7]. These methods are
variants of the method proposed by the same author.
In this phase they extend the feature set of the
fingerpring by incorporating local ridge, local
frequency, shape its. [1] contain the exhaustive review
in fingerprint matching and recognition. For further
reference reader may go through the
[8]-[33].
Two types of local minutia structure were proposed.
First one is based on the nearest neighbourhood while
the other one is based on the fixed radius. In the
nearest neighbourhood [6] approach, centre minutia is
characterized by the K- closest minutia which are
spatially arranged. This gives the fixed length
descriptor which can be matched very efficiently. The
later approach is given by the [7]. In this approach,
neighbours are defined as the all the neighbours which
are inside the radius R of the centre minutia. In this
approach, the length of the descriptor is not fixed
making it difficult to match the fingerprint locally. But
this approach shows better tolerant against missing
and spurious minutia.

E-ISSN: 2321-9637
33
2. PROPOSED METHODOLOGY
In this paper local descriptor based fingerprint
recognition is presented. Minutia cylinder code is used
as local descriptor. Block diagram of proposed
methodology is shown in figure 2.
First of all, with the help of scanner, Fingerprint image
of the input finger is registered. Minutia extraction
block is used to extract out the minutia from the
image.
With the help of minutia, a cylindrical code for
minutia is formed. Minutia cylindrical code represent
minutia local structure of the minutia distribution.
Once, minutia cylindrical code is generated for each
finger print then a database is created which contain
the minutia cylindrical codes of all the fingerprint
image. The last step of this method is matching or
testing. Next section describe the functioning of last
four block of this algorithm.
Fig.1 Basic Flow Diagram of Proposed Method
2.1. Minutia Extraction Process
In any fingerprint recognition system, Minutia
extraction is one of the important phase because the
performance of any fingerprint recognition system
depends on the how accurately minutia are extracted.
In this algorithm following steps have been adopted
for minutia extraction.
2.2 FP Image Enhancement
First step in any fingerprint recognition system is the
enhancement of the fingerprint image. This step is
adopted to improve the quality of the fingerprint
image so that all the minutia points become more clear
and extraction of the minutia points become easier.
Generally, Fingerprint image obtained by the
fingerprint scanner is noisy and of poor contrast.
Ridge and furrows are not visible due to the poor
contrast. Image enhancement increase the contrast of
ridge and furrows and also joined some of the broken
line which occurred in fingerprint image due to sensor
deficiency.
2.3 Histogram equalization
Histogram equalization basically adjust the intensity
distribution of the histogram and with this approach it
improve the global contrast of the image. This step
helps the lower contrast region to gain a higher
contrast while keeping the global contrast intact. This
method basically spread out the most frequently
occurred intensity value. Figure 3 represent the
histogram before and after applying histogram
equalization.
2.4 Fast Fourier Transform.
This process is accomplished for connecting the ridge
broken line . In this process first of all, Fingerprint
image is divided in to a block of 32 * 32 pixel and
then Fourier transform of this block is computed using
following formula.
for u = 0, 1, 2, ..., 31 and v = 0, 1, 2, ..., 31.
2.5 Image Binarization
This process is performed to convert the 8-bit gary
image to 1 bit image. In this, 0 represent the ridge and
1 represent the furrows. This step highlight the ridge
and furrows clearly in the image. A local adaptive
binarization method is used for this purpose.
2.6 Fingerprint Segmentation
This process is accomplished to extract out the region
of interest from the fingerprint image. In fingerprint
image, some of the area is not useful and carry
insignificant ridge and furrows. It is necessary to
discard this area . Segmentation process consist of two
Finger
Finger print
Scanner
Minutia
Extraction
Minutia Cylindrical code formation
Database Creation
Matching Algorithm
Fingerprint
Registration

E-ISSN: 2321-9637
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part i.e. Estimation of block direction, extraction of
region of interest by applying morphological
operation.
2.7 Estimation of Block Diagram
Block direction estimation is performed to separate
out background and foreground information. In finger
print image, foreground is represented by the ridges
and furrows while the background represent the no
information.
In order to compute the block diagram estimation, a
block of 16 *16 pixel is chosen and then block
direction is computed by using below mentioned
formula
tan2ß =
here
gx= Gradient along x-direction
gy= Gradient along y-direction
Certainity level for each block is then computed using
formula-
E =
Here
W= size of block (i.e. 16 in this case)
The value of E is compared with the threshold value to
decide whether the block comes under the
background( if E<threshold) or foreground (i.e if
E>threshold).
2.8 Morphological Operation for ROI extraction
In the last step morphological operation “opening” and
“closing” is applied to to extract the region of interest.
2.8.1 Ridge Thinning
This operation is applied to make the ridge width 1
pixel wide. This is performed by thinning operation.
2.8.2Minutia Marking
Next step is applied to mark the minutia by
applying a 3*3 window.
2.8.3False Minutia Removal
Inter-ridge distance is used for eliminating the false
minutia points which may affect the over all accuracy
of the fingerprint recognition system.
2.8.4Minutia Marking
At the final step each true minutia is marked with
three element. i.e. x-coordinate, y-coordinate and the
minutia direction or angle.
2.9 Minutia Cylindrical Code Information
Once the minutia information is obtained then the next
step is to form the minutia cylindrical code. In minutia
cylindrical code, a cylinder with 6 different layer is
used to represent the relative direction and distance
among different minutia. One such cylinder is shown
in figure 3.
Figure 3 represent the cylinder associated with the
minutia m={xm, ym, θm}. Radius of the cylinder is R
and xm and ym is the center of this cylinder. As
shown in the figure, a cuboid encloses the cylinder
whose base is aligned as per the direction of the
minutia i.e θm. The cuboid is divided in to a cell
where
The size and height of each cell is × and
respectively.
Where
Fig.2 Minutia Cylinder
Each cell of the cylinder can be accessed by three
coordinate i.e. i, j, k which represent the position of
the cell inside the cylinder. The value of i and j is
between 1 and Ns while the k takes the value between

E-ISSN: 2321-9637
35
1 to Nd. The height k of the cylinder represent the
angle which is represented by the
If
Represent the centre of the cell having indices i,j.
In MCC, the contribution of each minutia for
each cell is computed and is given by
Which is computed by the spatial contribution
and directional contribution
Where spatial contribution is given by
And directional contribution is given by
And
Is the neighbourhood of the .
Here is the neighbourhood radius and ds (p, m)
represent the Euclidean distance between minutia m
and point p as shown in the figure given below-
Fig.3 Neighbourhood Radius
Once the MCC for all the fingerprint image is
obtained then a database of MCC is created.
2.10 Fingerprint Matching
In order to match the two fingerprint MCC, Euclidean
distance is computed between two fingerprint MCC as
given in the below
Where
= MCC of the testing fingerprint image
= MCC of the database.
MCC for each fingerprint image is rotation and scale
invariants therefore it gives better result in even if the
fingerprint image is rotated or scaled.
The database MCC for which the value of Ed is less
than the certain threshold is considered as the
matching one and corresponding fingerprint image is
considered as the matching image
3. EXPERIMENTAL RESULTS
A database of fingerprint images comprises of the
200 different fingerprint images are created for testing
the performance of the proposed method. A simulation
program is designed with the help of MATLAB ver
2009B. The simulation program is tested in a
core2duo processor computer with 2GB RAM.

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36
Fig.4 Original Fingerprint Image
Figure 5 display the original fingerprint image while
figure 6 display the extracted minutia points of the
fingerprint image.
Fig.5 Extracted Minutia Points in fingerprint image
In order to evaluate the performance of this method,
statistical measure like FAR(False acceptance Ratio)
and FRR(False rejection ratio) is also computed and
tabulated in table 1.
Fr AR is computed using following formula
Table 1 FAR and FRR for different value of
Threshold (Without Rotation)
Threshold Value FAR(in %) FRR(in %)
6 6.9 0.07
7 7.4 0.05
8 9.2 0.05
9 11.9 0.03
10 13.1 0.02
Table2 FAR and FRR of rotated fingerprint image
for different value of Threshold
Threshold Value FAR(in %) FRR(in %)
6 6.3 0.12
7 7.6 0.08
8 10.1 0.07
9 12.1 0.03
10 13.9 0.03
Fig.6 Minutia Cylindrical Code of the fingerprint
image shown in figure 5
4. CONCLUSION
Since from its inception, finger-print matching area
has been in the search of some robust technique for
matching the fingerprint accurately. The requirement
of rotation , scale invariants re the need of the hour. In
this paper a MCC based fingerprint matching
algorithm is implemented and presented. The
performance of the proposed system is satisfactory as
clear from the table 1. Even after rotating and scaling
the fingerprint, proposed system is able to achieve
good accuracy.

E-ISSN: 2321-9637
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